

<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <meta name="Description" content="scikit-learn: machine learning in Python">

  
  <title>Multiclass sparse logisitic regression on newgroups20 &mdash; scikit-learn 0.22 documentation</title>
  
  <link rel="canonical" href="http://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.html" />

  
  <link rel="shortcut icon" href="../../_static/favicon.ico"/>
  

  <link rel="stylesheet" href="../../_static/css/vendor/bootstrap.min.css" type="text/css" />
  <link rel="stylesheet" href="../../_static/gallery.css" type="text/css" />
  <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
<script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
<script src="../../_static/jquery.js"></script> 
</head>
<body>
<nav id="navbar" class="sk-docs-navbar navbar navbar-expand-md navbar-light bg-light py-0">
  <div class="container-fluid sk-docs-container px-0">
      <a class="navbar-brand py-0" href="../../index.html">
        <img
          class="sk-brand-img"
          src="../../_static/scikit-learn-logo-small.png"
          alt="logo"/>
      </a>
    <button
      id="sk-navbar-toggler"
      class="navbar-toggler"
      type="button"
      data-toggle="collapse"
      data-target="#navbarSupportedContent"
      aria-controls="navbarSupportedContent"
      aria-expanded="false"
      aria-label="Toggle navigation"
    >
      <span class="navbar-toggler-icon"></span>
    </button>

    <div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
      <ul class="navbar-nav mr-auto">
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../install.html">Install</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../user_guide.html">User Guide</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../modules/classes.html">API</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../index.html">Examples</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../getting_started.html">Getting Started</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../tutorial/index.html">Tutorial</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../glossary.html">Glossary</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../developers/index.html">Development</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../faq.html">FAQ</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../related_projects.html">Related packages</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../roadmap.html">Roadmap</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../about.html">About us</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://github.com/scikit-learn/scikit-learn">GitHub</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://scikit-learn.org/dev/versions.html">Other Versions</a>
        </li>
        <li class="nav-item dropdown nav-more-item-dropdown">
          <a class="sk-nav-link nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">More</a>
          <div class="dropdown-menu" aria-labelledby="navbarDropdown">
              <a class="sk-nav-dropdown-item dropdown-item" href="../../getting_started.html">Getting Started</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../tutorial/index.html">Tutorial</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../glossary.html">Glossary</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../developers/index.html">Development</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../faq.html">FAQ</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../related_projects.html">Related packages</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../roadmap.html">Roadmap</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../about.html">About us</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="https://github.com/scikit-learn/scikit-learn">GitHub</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="https://scikit-learn.org/dev/versions.html">Other Versions</a>
          </div>
        </li>
      </ul>
      <div id="searchbox" role="search">
          <div class="searchformwrapper">
          <form class="search" action="../../search.html" method="get">
            <input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
            <input class="sk-search-text-btn" type="submit" value="Go" />
          </form>
          </div>
      </div>
    </div>
  </div>
</nav>
<div class="d-flex" id="sk-doc-wrapper">
    <input type="checkbox" name="sk-toggle-checkbox" id="sk-toggle-checkbox">
    <label id="sk-sidemenu-toggle" class="sk-btn-toggle-toc btn sk-btn-primary" for="sk-toggle-checkbox">Toggle Menu</label>
    <div id="sk-sidebar-wrapper" class="border-right">
      <div class="sk-sidebar-toc-wrapper">
        <div class="sk-sidebar-toc-logo">
          <a href="../../index.html">
            <img
              class="sk-brand-img"
              src="../../_static/scikit-learn-logo-small.png"
              alt="logo"/>
          </a>
        </div>
        <div class="btn-group w-100 mb-2" role="group" aria-label="rellinks">
            <a href="plot_lasso_model_selection.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="Lasso model selection: Cross-Validation / AIC / BIC">Prev</a><a href="../index.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="Examples">Up</a>
            <a href="plot_sgd_early_stopping.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="Early stopping of Stochastic Gradient Descent">Next</a>
        </div>
        <div class="alert alert-danger p-1 mb-2" role="alert">
          <p class="text-center mb-0">
          <strong>scikit-learn 0.22</strong><br/>
          <a href="http://scikit-learn.org/dev/versions.html">Other versions</a>
          </p>
        </div>
        <div class="alert alert-warning p-1 mb-2" role="alert">
          <p class="text-center mb-0">
            Please <a class="font-weight-bold" href="../../about.html#citing-scikit-learn"><string>cite us</string></a> if you use the software.
          </p>
        </div>
          <div class="sk-sidebar-toc">
            <ul>
<li><a class="reference internal" href="#">Multiclass sparse logisitic regression on newgroups20</a></li>
</ul>

          </div>
      </div>
    </div>
    <div id="sk-page-content-wrapper">
      <div class="sk-page-content container-fluid body px-md-3" role="main">
        
  <div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-linear-model-plot-sparse-logistic-regression-20newsgroups-py"><span class="std std-ref">here</span></a> to download the full example code or to run this example in your browser via Binder</p>
</div>
<div class="sphx-glr-example-title section" id="multiclass-sparse-logisitic-regression-on-newgroups20">
<span id="sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-20newsgroups-py"></span><h1>Multiclass sparse logisitic regression on newgroups20<a class="headerlink" href="#multiclass-sparse-logisitic-regression-on-newgroups20" title="Permalink to this headline">¶</a></h1>
<p>Comparison of multinomial logistic L1 vs one-versus-rest L1 logistic regression
to classify documents from the newgroups20 dataset. Multinomial logistic
regression yields more accurate results and is faster to train on the larger
scale dataset.</p>
<p>Here we use the l1 sparsity that trims the weights of not informative
features to zero. This is good if the goal is to extract the strongly
discriminative vocabulary of each class. If the goal is to get the best
predictive accuracy, it is better to use the non sparsity-inducing l2 penalty
instead.</p>
<p>A more traditional (and possibly better) way to predict on a sparse subset of
input features would be to use univariate feature selection followed by a
traditional (l2-penalised) logistic regression model.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">timeit</span>
<span class="kn">import</span> <span class="nn">warnings</span>

<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_20newsgroups_vectorized</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.exceptions</span> <span class="kn">import</span> <span class="n">ConvergenceWarning</span>

<span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="c1"># Author: Arthur Mensch</span>

<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="n">ConvergenceWarning</span><span class="p">,</span>
                        <span class="n">module</span><span class="o">=</span><span class="s2">&quot;sklearn&quot;</span><span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span>

<span class="c1"># We use SAGA solver</span>
<span class="n">solver</span> <span class="o">=</span> <span class="s1">&#39;saga&#39;</span>

<span class="c1"># Turn down for faster run time</span>
<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">10000</span>

<span class="c1"># Memorized fetch_rcv1 for faster access</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_20newsgroups_vectorized</span><span class="p">(</span><span class="s1">&#39;all&#39;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:</span><span class="n">n_samples</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span><span class="n">n_samples</span><span class="p">]</span>

<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span>
                                                    <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span>
                                                    <span class="n">stratify</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
                                                    <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">train_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Dataset 20newsgroup, train_samples=</span><span class="si">%i</span><span class="s1">, n_features=</span><span class="si">%i</span><span class="s1">, n_classes=</span><span class="si">%i</span><span class="s1">&#39;</span>
      <span class="o">%</span> <span class="p">(</span><span class="n">train_samples</span><span class="p">,</span> <span class="n">n_features</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">))</span>

<span class="n">models</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ovr&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;name&#39;</span><span class="p">:</span> <span class="s1">&#39;One versus Rest&#39;</span><span class="p">,</span> <span class="s1">&#39;iters&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">]},</span>
          <span class="s1">&#39;multinomial&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;name&#39;</span><span class="p">:</span> <span class="s1">&#39;Multinomial&#39;</span><span class="p">,</span> <span class="s1">&#39;iters&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">]}}</span>

<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
    <span class="c1"># Add initial chance-level values for plotting purpose</span>
    <span class="n">accuracies</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span> <span class="o">/</span> <span class="n">n_classes</span><span class="p">]</span>
    <span class="n">times</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    <span class="n">densities</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span>

    <span class="n">model_params</span> <span class="o">=</span> <span class="n">models</span><span class="p">[</span><span class="n">model</span><span class="p">]</span>

    <span class="c1"># Small number of epochs for fast runtime</span>
    <span class="k">for</span> <span class="n">this_max_iter</span> <span class="ow">in</span> <span class="n">model_params</span><span class="p">[</span><span class="s1">&#39;iters&#39;</span><span class="p">]:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;[model=</span><span class="si">%s</span><span class="s1">, solver=</span><span class="si">%s</span><span class="s1">] Number of epochs: </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span>
              <span class="p">(</span><span class="n">model_params</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">],</span> <span class="n">solver</span><span class="p">,</span> <span class="n">this_max_iter</span><span class="p">))</span>
        <span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">solver</span><span class="p">,</span>
                                <span class="n">multi_class</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
                                <span class="n">penalty</span><span class="o">=</span><span class="s1">&#39;l1&#39;</span><span class="p">,</span>
                                <span class="n">max_iter</span><span class="o">=</span><span class="n">this_max_iter</span><span class="p">,</span>
                                <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span>
                                <span class="p">)</span>
        <span class="n">t1</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span>
        <span class="n">lr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
        <span class="n">train_time</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">t1</span>

        <span class="n">y_pred</span> <span class="o">=</span> <span class="n">lr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
        <span class="n">accuracy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y_pred</span> <span class="o">==</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">/</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">lr</span><span class="o">.</span><span class="n">coef_</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span>
        <span class="n">accuracies</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">accuracy</span><span class="p">)</span>
        <span class="n">densities</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">density</span><span class="p">)</span>
        <span class="n">times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">train_time</span><span class="p">)</span>
    <span class="n">models</span><span class="p">[</span><span class="n">model</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">times</span>
    <span class="n">models</span><span class="p">[</span><span class="n">model</span><span class="p">][</span><span class="s1">&#39;densities&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">densities</span>
    <span class="n">models</span><span class="p">[</span><span class="n">model</span><span class="p">][</span><span class="s1">&#39;accuracies&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">accuracies</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy for model </span><span class="si">%s</span><span class="s1">: </span><span class="si">%.4f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">accuracies</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%%</span><span class="s1"> non-zero coefficients for model </span><span class="si">%s</span><span class="s1">, &#39;</span>
          <span class="s1">&#39;per class:</span><span class="se">\n</span><span class="s1"> </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">densities</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Run time (</span><span class="si">%i</span><span class="s1"> epochs) for model </span><span class="si">%s</span><span class="s1">:&#39;</span>
          <span class="s1">&#39;</span><span class="si">%.2f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">model_params</span><span class="p">[</span><span class="s1">&#39;iters&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">model</span><span class="p">,</span> <span class="n">times</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>

<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>

<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
    <span class="n">name</span> <span class="o">=</span> <span class="n">models</span><span class="p">[</span><span class="n">model</span><span class="p">][</span><span class="s1">&#39;name&#39;</span><span class="p">]</span>
    <span class="n">times</span> <span class="o">=</span> <span class="n">models</span><span class="p">[</span><span class="n">model</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">]</span>
    <span class="n">accuracies</span> <span class="o">=</span> <span class="n">models</span><span class="p">[</span><span class="n">model</span><span class="p">][</span><span class="s1">&#39;accuracies&#39;</span><span class="p">]</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">accuracies</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span>
            <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Model: </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Train time (s)&#39;</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Test accuracy&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">&#39;Multinomial vs One-vs-Rest Logistic L1</span><span class="se">\n</span><span class="s1">&#39;</span>
             <span class="s1">&#39;Dataset </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="s1">&#39;20newsgroups&#39;</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mf">0.85</span><span class="p">)</span>
<span class="n">run_time</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Example run in </span><span class="si">%.3f</span><span class="s1"> s&#39;</span> <span class="o">%</span> <span class="n">run_time</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-linear-model-plot-sparse-logistic-regression-20newsgroups-py">
<div class="binder-badge docutils container">
<a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.ipynb"><img alt="https://mybinder.org/badge_logo.svg" src="https://mybinder.org/badge_logo.svg" width="150px" /></a>
</div>
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/9e0c5410e772a5deb9b4ca66201ad8d5/plot_sparse_logistic_regression_20newsgroups.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_sparse_logistic_regression_20newsgroups.py</span></code></a></p>
</div>
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7f0d1a6c9b15a3e6cce8554ba88c3738/plot_sparse_logistic_regression_20newsgroups.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_sparse_logistic_regression_20newsgroups.ipynb</span></code></a></p>
</div>
</div>
<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
</div>


      </div>
    <div class="container">
      <footer class="sk-content-footer">
            &copy; 2007 - 2019, scikit-learn developers (BSD License).
          <a href="../../_sources/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.rst.txt" rel="nofollow">Show this page source</a>
      </footer>
    </div>
  </div>
</div>
<script src="../../_static/js/vendor/bootstrap.min.js"></script>

<script>
    window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)};ga.l=+new Date;
    ga('create', 'UA-22606712-2', 'auto');
    ga('set', 'anonymizeIp', true);
    ga('send', 'pageview');
</script>
<script async src='https://www.google-analytics.com/analytics.js'></script>


<script>
$(document).ready(function() {
    /* Add a [>>>] button on the top-right corner of code samples to hide
     * the >>> and ... prompts and the output and thus make the code
     * copyable. */
    var div = $('.highlight-python .highlight,' +
                '.highlight-python3 .highlight,' +
                '.highlight-pycon .highlight,' +
		'.highlight-default .highlight')
    var pre = div.find('pre');

    // get the styles from the current theme
    pre.parent().parent().css('position', 'relative');
    var hide_text = 'Hide prompts and outputs';
    var show_text = 'Show prompts and outputs';

    // create and add the button to all the code blocks that contain >>>
    div.each(function(index) {
        var jthis = $(this);
        if (jthis.find('.gp').length > 0) {
            var button = $('<span class="copybutton">&gt;&gt;&gt;</span>');
            button.attr('title', hide_text);
            button.data('hidden', 'false');
            jthis.prepend(button);
        }
        // tracebacks (.gt) contain bare text elements that need to be
        // wrapped in a span to work with .nextUntil() (see later)
        jthis.find('pre:has(.gt)').contents().filter(function() {
            return ((this.nodeType == 3) && (this.data.trim().length > 0));
        }).wrap('<span>');
    });

    // define the behavior of the button when it's clicked
    $('.copybutton').click(function(e){
        e.preventDefault();
        var button = $(this);
        if (button.data('hidden') === 'false') {
            // hide the code output
            button.parent().find('.go, .gp, .gt').hide();
            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
            button.css('text-decoration', 'line-through');
            button.attr('title', show_text);
            button.data('hidden', 'true');
        } else {
            // show the code output
            button.parent().find('.go, .gp, .gt').show();
            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
            button.css('text-decoration', 'none');
            button.attr('title', hide_text);
            button.data('hidden', 'false');
        }
    });

	/*** Add permalink buttons next to glossary terms ***/
	$('dl.glossary > dt[id]').append(function() {
		return ('<a class="headerlink" href="#' +
			    this.getAttribute('id') +
			    '" title="Permalink to this term">¶</a>');
	});
  /*** Hide navbar when scrolling down ***/
  // Returns true when headerlink target matches hash in url
  (function() {
    hashTargetOnTop = function() {
        var hash = window.location.hash;
        if ( hash.length < 2 ) { return false; }

        var target = document.getElementById( hash.slice(1) );
        if ( target === null ) { return false; }

        var top = target.getBoundingClientRect().top;
        return (top < 2) && (top > -2);
    };

    // Hide navbar on load if hash target is on top
    var navBar = document.getElementById("navbar");
    var navBarToggler = document.getElementById("sk-navbar-toggler");
    var navBarHeightHidden = "-" + navBar.getBoundingClientRect().height + "px";
    var $window = $(window);

    hideNavBar = function() {
        navBar.style.top = navBarHeightHidden;
    };

    showNavBar = function() {
        navBar.style.top = "0";
    }

    if (hashTargetOnTop()) {
        hideNavBar()
    }

    var prevScrollpos = window.pageYOffset;
    hideOnScroll = function(lastScrollTop) {
        if (($window.width() < 768) && (navBarToggler.getAttribute("aria-expanded") === 'true')) {
            return;
        }
        if (lastScrollTop > 2 && (prevScrollpos <= lastScrollTop) || hashTargetOnTop()){
            hideNavBar()
        } else {
            showNavBar()
        }
        prevScrollpos = lastScrollTop;
    };

    /*** high preformance scroll event listener***/
    var raf = window.requestAnimationFrame ||
        window.webkitRequestAnimationFrame ||
        window.mozRequestAnimationFrame ||
        window.msRequestAnimationFrame ||
        window.oRequestAnimationFrame;
    var lastScrollTop = $window.scrollTop();

    if (raf) {
        loop();
    }

    function loop() {
        var scrollTop = $window.scrollTop();
        if (lastScrollTop === scrollTop) {
            raf(loop);
            return;
        } else {
            lastScrollTop = scrollTop;
            hideOnScroll(lastScrollTop);
            raf(loop);
        }
    }
  })();
});

</script>
    
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
    
</body>
</html>